Tidy Data

Tidy data…

• is rectangular.
• has variables as rows and observations as columns.
• has different formats for different tasks.

R4DS

Consequences of Messy Data

Illustration by Allison Horst

• Tidy: use the same tools in similar ways for different datasets.
• Messy: create unique tools that are difficult to generalize.

Creating Tidy Data

We may need to transform our data to turn it into the version of tidy that is best for a task at hand.

Illustration by Allison Horst

Creating Tidy Data

We want to look at mean cereal nutrients based on shelf.

• The data are in a wide format – a separate column for each nutrient.
• Transforming the data will make plotting easier.

library(liver)
data(cereal)
head(cereal)

Creating Tidy Data

cereal_wide <- cereal |> 
  group_by(shelf) |> 
  summarise(across(calories:vitamins, mean))

my_colors <- c("calories_col" = "steelblue", "sugars_col" = "orange3")

cereal_wide |> 
  ggplot() +
  geom_point(aes(x = shelf, y = calories, color = "calories_col")) +
  geom_line(aes(x = shelf, y = calories, color = "calories_col")) + 
  geom_point(aes(x = shelf, y = sugars, color = "sugars_col")) +
  geom_line(aes(x = shelf, y = sugars, color = "sugars_col")) +
  scale_color_manual(values = my_colors, labels = names(my_colors)) +
  labs(x = "Shelf", y = "", subtitle = "Mean Amount", color = "Nutrient")

cereal_long<- cereal |> 
  pivot_longer(cols = calories:vitamins,
               names_to = "Nutrient",
               values_to = "Amount") |> 
  group_by(shelf, Nutrient) |> 
  summarise(mean_amount = mean(Amount))

cereal_long |> 
  ggplot(aes(x = shelf, 
             y = mean_amount, 
             color = Nutrient)) +
  geom_point() +
  geom_line() +
  labs(x = "Shelf", y = "", subtitle = "Mean Amount")

Manual Method

Consider daily rainfall observed in SLO in January 2023.

• The data is in a human-friendly form (like a calendar).
• Each week has a row, and each day has a column.

Data source

How would you manually convert this to long format?

Manual Method: Steps

Computational Approach

We can use pivot_longer() to turn a wide dataset into a long(er) dataset.

pivot_longer()

Take a wide dataset and turn it into a long daaset.

• cols – specify the columns that should be pivoted.
  • Do not include the names of ID columns (columns to not be pivoted).
• names_to – the name of the new column containing the old column names.
• values_to – the name of the new column containing the old column values.

pivot_longer()

library(readxl)
slo_rainfall <- read_xlsx("data/2023-rainfall-slo.xlsx")

slo_rainfall |> 
  mutate(across(Sunday:Saturday, as.numeric)) |> 
  pivot_longer(cols      = Sunday:Saturday,
               names_to  = "Day_of_Week",
               values_to = "Daily_Rainfall")

pivot_wider()

Take a long dataset and turn it into a wide daaset.

• id_cols – specify the column(s) that contain the ID for unique rows in the wide dataset.
• names_from – the name of the column containing the new column names.
• values_from – the name of the column containing the new column values.

pivot_wider()

We calculate the mean amount of protein for cereals on each shelpf and for each manufacturer.

mean_protein <- cereal |> 
  group_by(manuf, shelf) |> 
  summarize(mean_protein = mean(protein))

pivot_wider()

mean_protein |> 
  arrange(shelf) |> 
  pivot_wider(id_cols = manuf,
              names_from = shelf,
              values_from = mean_protein)

Better names in pivot_wider()

mean_protein |> 
  arrange(shelf) |> 
  pivot_wider(id_cols = manuf,
              names_from = shelf,
              values_from = mean_protein,
              names_prefix = "Shelf_")

Relational Data

Multiple, interconnected tables of data are called relational.

• It is the relation between data sets, not just the individual data sets themselves, that are important.

IMDb movie relational data

Data Joins

We can combine (join) data tables based on their relations.

Keys

A key uniquely identifies an observation in a data set.

• To combine (join) two datasets, the key needs to be present in both.

inner_join()

Keeps obsertvations when their keys are present in both datasets.

inner_join(): IMDb Example

directors_genres

movies_directors

inner_join(directors_genres, movies_directors)

inner_join(): IMDb Example

What if our key does not have the same name?

directors_genres

directors

inner_join(directors_genres, 
           directors, 
           by = c("director_id" = "id"))

Mutating Joins

• left_join() – keep only (and all) observations in the left data set

• right_join() – keep only (and all) observations in the right data set

• full_join() – keep all observations in both data sets

Mutating Joins

Which directors would remain for each of the following?

directors_genres |> 
  distinct(director_id)

movies_directors |> 
  distinct(director_id)

Filtering Joins: semi_join()

Keeps observations when their keys are present in both datasets, but only keeps variables from the first dataset.

Filtering Joins: semi_join()

semi_join(directors_genres, movies_directors)

directors_genres |>
  filter(director_id %in% movies_directors$director_id)

Filtering Joins: anti_join()

Removes observations when their keys are present in both datasets, and only keeps variables from the first dataset.

Filtering Joins: anti_join()

anti_join(directors_genres, movies_directors)

directors_genres |>
  filter(!director_id %in% movies_directors$director_id)

Piping Joins

Remember: the dataset you pipe in becomes the first argument of the function you are piping into!

inner_join(directors_genres, movies_directors)

…is equivalent to…

directors_genres |> 
  inner_join(movies_directors)

PA 4: Military Spending

Today you will be tidying messy data to explore the relationship between countries of the world and military spending.

• Due Wednesday, 4/26 at 10:00am

Bonus Challenge: Murder in SQL City

For this challenge, you will be using table joins to solve a murder mystery!

• Due Monday, 5/8 at 11:59pm

To do…

• PA 4: Military Spending
  • Due Wednesday, 4/26 at 10:00am
• Bonus Challenge: Murder Mystery in SQL City
  • Due Monday, 5/8 at 11:59pm

Wednesday, January 25th

Today we will…

• Review Lab 3
• Review PA 4: Military Spending
• Housekeeping items…
  • Providing References in Labs + Challenges
  • Clean variable names
  • Lifecycle stages
  • dplyr package updates
  • Saving & Piping Data Joins
• Extensions to Relational Data
• Lab 4: Avocado Prices
• Challenge 3: Avocado Toast Ate My Mortgage

Getting Help and using Chat GPT

Lab 3: Distinct number of words

count(distinct(hiphop_clean, word))

hiphop_clean |> 
  distinct(word) |> 
  count()

n_distinct(hiphop_clean$word)

hiphop_clean |> 
  pull(word) |> 
  n_distinct()

Clean variable names with library(janitor)

Converts all names of variables in a data set to snake_case.

names(military)

 [1] "Country"        "Notes"          "Reporting year" "1988"          
 [5] "1989"           "1990"           "1991"           "1992"          
 [9] "1993"           "1994"           "1995"           "1996"          
[13] "1997"           "1998"           "1999"           "2000"          
[17] "2001"           "2002"           "2003"           "2004"          
[21] "2005"           "2006"           "2007"           "2008"          
[25] "2009"           "2010"           "2011"           "2012"          
[29] "2013"           "2014"           "2015"           "2016"          
[33] "2017"           "2018"           "2019"          

library(janitor)
military_clean_names <- military |> 
  clean_names()

names(military_clean_names)

 [1] "country"        "notes"          "reporting_year" "x1988"         
 [5] "x1989"          "x1990"          "x1991"          "x1992"         
 [9] "x1993"          "x1994"          "x1995"          "x1996"         
[13] "x1997"          "x1998"          "x1999"          "x2000"         
[17] "x2001"          "x2002"          "x2003"          "x2004"         
[21] "x2005"          "x2006"          "x2007"          "x2008"         
[25] "x2009"          "x2010"          "x2011"          "x2012"         
[29] "x2013"          "x2014"          "x2015"          "x2016"         
[33] "x2017"          "x2018"          "x2019"         

Lifceycle stages

Learn more about lifecycle stages of packages, functions, function arguments in R.

Deprecated Functions: Military Spending Example

military_clean |> 
  filter(across(Notes:`2019`, is.na)) |> 
  slice_head(n = 3)

Warning: Using `across()` in `filter()` was deprecated in dplyr 1.0.8.
ℹ Please use `if_any()` or `if_all()` instead.

# A tibble: 3 × 35
  Country      Notes `Reporting year` `1988` `1989` `1990` `1991` `1992` `1993`
  <chr>        <chr> <chr>            <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
1 Africa       <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
2 North Africa <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
3 Sub-Saharan  <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
# ℹ 26 more variables: `1994` <chr>, `1995` <chr>, `1996` <chr>, `1997` <chr>,
#   `1998` <chr>, `1999` <chr>, `2000` <chr>, `2001` <chr>, `2002` <chr>,
#   `2003` <chr>, `2004` <chr>, `2005` <chr>, `2006` <chr>, `2007` <chr>,
#   `2008` <chr>, `2009` <chr>, `2010` <chr>, `2011` <chr>, `2012` <chr>,
#   `2013` <chr>, `2014` <chr>, `2015` <chr>, `2016` <chr>, `2017` <chr>,
#   `2018` <chr>, `2019` <chr>

military_clean |>
  filter(if_all(Notes:`2019`, ~ is.na(.x))) |> 
  slice_head(n = 3)

# A tibble: 3 × 35
  Country      Notes `Reporting year` `1988` `1989` `1990` `1991` `1992` `1993`
  <chr>        <chr> <chr>            <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
1 Africa       <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
2 North Africa <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
3 Sub-Saharan  <NA>  <NA>             <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
# ℹ 26 more variables: `1994` <chr>, `1995` <chr>, `1996` <chr>, `1997` <chr>,
#   `1998` <chr>, `1999` <chr>, `2000` <chr>, `2001` <chr>, `2002` <chr>,
#   `2003` <chr>, `2004` <chr>, `2005` <chr>, `2006` <chr>, `2007` <chr>,
#   `2008` <chr>, `2009` <chr>, `2010` <chr>, `2011` <chr>, `2012` <chr>,
#   `2013` <chr>, `2014` <chr>, `2015` <chr>, `2016` <chr>, `2017` <chr>,
#   `2018` <chr>, `2019` <chr>

dplyr updates

There was an update to the dplyr package to Version 1.1.0 on Sunday 1/29/2023.

See changelog for updates

group_by() vs .by = argument

across()

military |> 
  mutate(across(`1988`:`2019`, na_if, y = ". ."))


military |> 
  mutate(across(`1988`:`2019`, ~ na_if(.x, y = ". .")))

join_by()

Saving & Piping data joins

genres_movies_joined <- inner_join(directors_genres, movies_directors)

genres_movies_joined <- directors_genres |> 
  inner_join(movies_directors)

IMDb Movies Data

Multiple tables of data are called relational data because it is the relations, not just the individual data sets, that are important.

Keys:

• Uniquely identifies an observation in a data set
• Relate data sets to each other

Joining Multiple Data Sets

movies_directors

# A tibble: 41 × 2
   director_id movie_id
         <dbl>    <dbl>
 1         429   300229
 2        2931   254943
 3        9247   124110
 4       11652    10920
 5       11652   333856
 6       14927   192017
 7       15092   109093
 8       15092   237431
 9       15093   109093
10       15093   237431
# ℹ 31 more rows

directors

# A tibble: 34 × 3
      id first_name   last_name
   <dbl> <chr>        <chr>    
 1   429 Andrew       Adamson  
 2  2931 Darren       Aronofsky
 3  9247 Zach         Braff    
 4 11652 James (I)    Cameron  
 5 14927 Ron          Clements 
 6 15092 Ethan        Coen     
 7 15093 Joel         Coen     
 8 15901 Francis Ford Coppola  
 9 15906 Sofia        Coppola  
10 16816 Cameron      Crowe    
# ℹ 24 more rows

movies

# A tibble: 36 × 4
       id name             year  rank
    <dbl> <chr>           <dbl> <dbl>
 1  10920 Aliens           1986  8.20
 2  17173 Animal House     1978  7.5 
 3  18979 Apollo 13        1995  7.5 
 4  30959 Batman Begins    2005 NA   
 5  46169 Braveheart       1995  8.30
 6 109093 Fargo            1996  8.20
 7 111813 Few Good Men, A  1992  7.5 
 8 112290 Fight Club       1999  8.5 
 9 116907 Footloose        1984  5.80
10 124110 Garden State     2004  8.30
# ℹ 26 more rows

join_one_data <- movies_directors |> 
  inner_join(directors, 
             by = c("director_id" = "id")
             )
join_one_data

# A tibble: 41 × 4
   director_id movie_id first_name last_name
         <dbl>    <dbl> <chr>      <chr>    
 1         429   300229 Andrew     Adamson  
 2        2931   254943 Darren     Aronofsky
 3        9247   124110 Zach       Braff    
 4       11652    10920 James (I)  Cameron  
 5       11652   333856 James (I)  Cameron  
 6       14927   192017 Ron        Clements 
 7       15092   109093 Ethan      Coen     
 8       15092   237431 Ethan      Coen     
 9       15093   109093 Joel       Coen     
10       15093   237431 Joel       Coen     
# ℹ 31 more rows

directors_movie_ids <- movies_directors |> 
  inner_join(directors, 
             by = join_by(id == director_id)
             )

join_two_data <- movies_directors |> 
  inner_join(directors, 
             by = c("director_id" = "id")
             ) |> 
  inner_join(movies,
             by = c("movie_id" = "id")
             ) |> 
  rename(movie_name = name)
join_two_data

# A tibble: 41 × 7
   director_id movie_id first_name last_name movie_name               year  rank
         <dbl>    <dbl> <chr>      <chr>     <chr>                   <dbl> <dbl>
 1         429   300229 Andrew     Adamson   Shrek                    2001  8.10
 2        2931   254943 Darren     Aronofsky Pi                       1998  7.5 
 3        9247   124110 Zach       Braff     Garden State             2004  8.30
 4       11652    10920 James (I)  Cameron   Aliens                   1986  8.20
 5       11652   333856 James (I)  Cameron   Titanic                  1997  6.90
 6       14927   192017 Ron        Clements  Little Mermaid, The      1989  7.30
 7       15092   109093 Ethan      Coen      Fargo                    1996  8.20
 8       15092   237431 Ethan      Coen      O Brother, Where Art T…  2000  7.80
 9       15093   109093 Joel       Coen      Fargo                    1996  8.20
10       15093   237431 Joel       Coen      O Brother, Where Art T…  2000  7.80
# ℹ 31 more rows

Joining on Multiple Variables

Using the hiphop data from Lab 3…

music

# A tibble: 10,752 × 6
   word          subj   folk  rock country   pop
   <chr>         <chr> <dbl> <dbl>   <dbl> <dbl>
 1 [to be] ghost p15       0     1       3     4
 2 [to be] ghost p53       0     0       1     3
 3 [to be] ghost p136      0     0       0     0
 4 [to be] ghost p36       1     1       2     2
 5 [to be] ghost p118      0     1       0     3
 6 [to be] ghost p106      0     0       1     1
 7 [to be] ghost p70       2     0       0     0
 8 [to be] ghost p107      0     1       3     1
 9 [to be] ghost p102      0     0       0     0
10 [to be] ghost p83       0     4       4     4
# ℹ 10,742 more rows

familiarity

# A tibble: 10,752 × 3
   word          participant familiarity
   <chr>         <chr>             <dbl>
 1 [to be] ghost p15                   1
 2 [to be] ghost p53                   1
 3 [to be] ghost p136                  1
 4 [to be] ghost p36                   1
 5 [to be] ghost p118                  1
 6 [to be] ghost p106                  1
 7 [to be] ghost p70                   1
 8 [to be] ghost p107                  1
 9 [to be] ghost p102                  5
10 [to be] ghost p83                   1
# ℹ 10,742 more rows

# A tibble: 10,752 × 7
   word          subj   folk  rock country   pop familiarity
   <chr>         <chr> <dbl> <dbl>   <dbl> <dbl>       <dbl>
 1 [to be] ghost p15       0     1       3     4           1
 2 [to be] ghost p53       0     0       1     3           1
 3 [to be] ghost p136      0     0       0     0           1
 4 [to be] ghost p36       1     1       2     2           1
 5 [to be] ghost p118      0     1       0     3           1
 6 [to be] ghost p106      0     0       1     1           1
 7 [to be] ghost p70       2     0       0     0           1
 8 [to be] ghost p107      0     1       3     1           1
 9 [to be] ghost p102      0     0       0     0           5
10 [to be] ghost p83       0     4       4     4           1
# ℹ 10,742 more rows

music_wordfam <- music |> 
  full_join(familiarity,
            by = join_by(word == word, 
                         subj == participant)
            )
music_wordfam 

Lab + Challenge

Lab 4: Avocado Prices + Challenge 4: Avocado Toast Ate My Mortgage

Handy Helpers

rename() – Change names of columns

separate() – Separate values of a variable

Filtering Joins

semi_join(): Keeps values found in another data set

anti_join(): Keeps values not found in another data set

Workflow

1. Load packages + read in original data

library(tidyverse)
data_original <- read_csv(file = "path/to/datal.csv")

2. Clean data – save your changes! This is now your new “master” data set

data_clean <- data_original |> 
  mutate(across(x1:x5, ~ as.factor(.x))) |> 
  mutate(new_var <- if_else(...))

3. If you need subsets, create those from your new “master” data for the specific tasks.

demographics_subj <- data_clean |> 
  distinct(subj, keep_all = TRUE)

4. Output only the the information you want to include in your assignment.

To do…

• Lab 4: Avocado Prices
  • Due Friday, 2/3 at 11:59pm
• Challenge 4: Avocado Toast Ate My Mortgage
  • Due Saturday, 2/4 at 11:59pm
• Read Chapter 5: Special Data Types
  • Concept Check 5.1 + 5.2 + 5.3 due Monday (2/6) at 8:00am
• Bonus Challenge: Murder Mystery in SQL City
  • Due Sunday 2/12 at 11:59pm